While existing UDA algorithms have been successful in reducing domain gap, the vast majority of these algorithms require sharing data between the source and target domains to enforce distribution alignment. This requirement limits the applicability of most existing works when sharing data may not be possible, e.g., sharing data is heavily regulated in healthcare domains due to privacy or security concerns. Until recently, there has been little exploration of UDA when access to the source domain is limited [1]}, [2]}, [3]}. These works benefit from generative adversarial learning to remember the source domain. However, both address UDA for only classification tasks which limits their applicability to the problem of organ semantic segmentation [4]}.
